Hyperspectral Images Classification and Dimensionality Reduction using spectral interaction and SVM classifier

Over the past decades, the hyperspectral remote sensing technology development has attracted growing interest among scientists in various domains. The rich and detailed spectral information provided by the hyperspectral sensors has improved the monitoring and detection capabilities of the earth surface substances. However, the high dimensionality of the hyperspectral images (HSI) is one of the main challenges for the analysis of the collected data. The existence of noisy, redundant and irrelevant bands increases the computational complexity, induce the Hughes phenomenon and decrease the target's classification accuracy. Hence, the dimensionality reduction is an essential step to face the dimensionality challenges. In this paper, we propose a novel filter approach based on the maximization of the spectral interaction measure and the support vector machines for dimensionality reduction and classification of the HSI. The proposed Max Relevance Max Synergy (MRMS) algorithm evaluates the relevance of every band through the combination of spectral synergy, redundancy and relevance measures. Our objective is to select the optimal subset of synergistic bands providing accurate classification of the supervised scene materials. Experimental results have been performed using three different hyperspectral datasets: "Indiana Pine", "Pavia University" and "Salinas" provided by the "NASA-AVIRIS" and the "ROSIS" spectrometers. Furthermore, a comparison with the state of the art band selection methods has been carried out in order to demonstrate the robustness and efficiency of the proposed approach. Keywords: Hyperspectral images, remote sensing, dimensionality reduction, classification, synergic, correlation, spectral interaction information, mutual inform

A Novel Approach for Dimensionality Reduction and Classification of Hyperspectral Images based on Normalized Synergy

During the last decade, hyperspectral images have attracted increasing interest from researchers worldwide. They provide more detailed information about an observed area and allow an accurate target detection and precise discrimination of objects compared to classical RGB and multispectral images. Despite the great potentialities of hyperspectral technology, the analysis and exploitation of the large volume data remain a challenging task. The existence of irrelevant redundant and noisy images decreases the classification accuracy. As a result, dimensionality reduction is a mandatory step in order to select a minimal and effective images subset. In this paper, a new filter approach normalized mutual synergy (NMS) is proposed in order to detect relevant bands that are complementary in the class prediction better than the original hyperspectral cube data. The algorithm consists of two steps: images selection through normalized synergy information and pixel classification. The proposed approach measures the discriminative power of the selected bands based on a combination of their maximal normalized synergic information, minimum redundancy and maximal mutual information with the ground truth. A comparative study using the support vector machine (SVM) and k-nearest neighbor (KNN) classifiers is conducted to evaluate the proposed approach compared to the state of art band selection methods. Experimental results on three benchmark hyperspectral images proposed by the NASA "Aviris Indiana Pine", "Salinas" and "Pavia University" demonstrated the robustness, effectiveness and the discriminative power of the proposed approach over the literature approaches. Keywords: Hyperspectral images; target detection; pixel classification; dimensionality reduction; band selection; information theory; mutual information; normalized synergy